class_docstr: Conditional Random Field layer (tf.keras)
  `CRF` can be used as the last layer in a network (as a classifier). Input shape (features)
  must be equal to the number of classes the CRF can predict (a linear layer is recommended).
  
  Note: the loss and accuracy functions of networks using `CRF` must
  use the provided loss and accuracy functions (denoted as loss and viterbi_accuracy)
  as the classification of sequences are used with the layers internal weights.
  
  Args:
      num_labels (int): the number of labels to tag each temporal input.
  
  Input shape:
      nD tensor with shape `(batch_size, sentence length, num_classes)`.
  
  Output shape:
      nD tensor with shape: `(batch_size, sentence length, num_classes)`.
code: def compute_output_shape(self, input_shape):
      tf.TensorShape(input_shape).assert_has_rank(3)
      return input_shape[:2] + (self.output_dim,)
  
file_path: nlp-architect/nlp_architect/nn/tensorflow/python/keras/layers/crf.py
imports: import tensorflow
label_desc: Use image node_id 6 for calling the CRF obj's underlying member method code with example usage: obj.compute_output_shape(input_shape) and returns: unknown